Less Data, More Knowledge: Building Next Generation Semantic Communication Networks

Christina Chaccour; Walid Saad; Merouane Debbah; Zhu Han; H. Vincent Poor

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Less Data, More Knowledge: Building Next Generation Semantic Communication Networks

Christina Chaccour, Walid Saad, Merouane Debbah, Zhu Han, H. Vincent Poor

arXiv:2211.14343v1Nov 25, 2022cs.AIcs.ITcs.LGcs.NI

TL;DR

The tutorial addresses the limited foundations for scalable semantic communication networks and proposes a rigorous vision grounded in AI, causal reasoning, and communication theory. It develops semantic representations and languages that turn transmitters and receivers into teachers and apprentices, and introduces reasoning-capacity concepts alongside a roadmap for large-scale networks.

Problem
Existing semantic communication research and wireless AI frameworks have limited generalizability, reasoning ability, and capacity to organize causal and associational structure into reusable knowledge.
Method
The tutorial proposes semantic languages built from minimal representations, teacher–apprentice communication, causal reasoning, and semantic communication metrics for reasoning-driven networks.
Results
The tutorial presents a comprehensive roadmap for building, analyzing, and deploying scalable semantic communication networks, including reasoning-capacity measures that can extend beyond Shannon’s limit through computing.
Takeaways & Limitations
Semantic communication should be developed as a rigorous intersection of AI, causal reasoning, communication theory, and networking rather than as an incremental extension of existing techniques.

Abstract

from arXiv · show

Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, and communication theory. We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones. Subsequently, we highlight the necessity of creating semantic representations of data that satisfy the key properties of minimalism, generalizability, and efficiency so as to do more with less. We then explain how those representations can form the basis a so-called semantic language. By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice. Then, we define the concept of reasoning by investigating the fundamentals of causal representation learning and their role in designing semantic communication networks. We demonstrate that reasoning faculties are majorly characterized by the ability to capture causal and associational relationships in datastreams. For such reasoning-driven networks, we propose novel and essential semantic communication metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the convergence of computing and communication. Finally, we explain how semantic communications can be scaled to large-scale networks (6G and beyond). In a nutshell, we expect this tutorial to provide a comprehensive reference on how to properly build, analyze, and deploy future semantic communication networks.

I. INTRODUCTION

The tutorial argues that future wireless systems should move beyond data-driven, bit-pipe designs toward knowledge-driven, reasoning-based semantic networks. These networks use meaning, context, causal structure, and accumulated knowledge to make communication more intelligent and potentially less dependent on channel conditions.

Motivation: 6G applications require rethinking the entire wireless architecture rather than relying on incremental improvements to spectrum and multi-antenna technologies.The motivation includes emerging applications such as the metaverse, holographic teleportation, digital twins, and Industry 5.0.
From data-driven to knowledge-driven networks: Current AI-based wireless approaches remain mainly data-driven or information-driven and fail to exploit knowledge accumulated during system operation.Examples include reliance on spectrum data, channel data, and QoS values for network adjustment.
From data-driven to knowledge-driven networks: Reasoning-driven AI-native networks use causality and stochasticity to identify structure, deduce logical connections, and mitigate semantic noise.The envisioned network can grow with cumulative knowledge and execute operations beyond simple AI augmentation of existing components.
Semantic communication: Semantic communication transforms radio nodes from passive bit pipes into intelligent agents that extract meaning, use context, and generate conclusions.This shift can reduce repetitive transmissions and improve robustness by making the receiver less passive and more symmetric with the transmitter.
From a bit-driven transmitter to a knowledge-driven teacher: A semantic transmitter acts as a teacher by disentangling multiple semantic content elements and encoding each into a minimal representation.The paper contrasts this with conventional transmitters, which do not identify or separate underlying semantic structures or modalities.

2) From a bit-driven receiver to a knowledge-driven apprentice:

Semantic communication replaces the bit-pipeline with semantic representations and languages that support a teacher–apprentice interaction grounded in knowledge and reasoning. These representations are designed to be minimal, generalizable, and efficient, while causal reasoning and new metrics extend how such systems are built and evaluated.

From a bit-driven receiver to a knowledge-driven apprentice:: The teacher identifies semantic content in raw datastreams and learns its representation, while the apprentice interprets representations, maps them to content, and draws conclusions from accumulated knowledge.This reframes the traditional transmitter and receiver as complementary learning and reasoning roles.
From a bit-driven receiver to a knowledge-driven apprentice:: A semantic representation is the smallest distinct meaningful element, while a series of representations forms a semantic language modeled less on syntax and pragmatics than natural languages.The language is intended to automate processes while preserving meaningful structure.
From a bit-driven receiver to a knowledge-driven apprentice:: Semantic representations should be minimal, generalizable across distributions, domains, and contexts, and efficient to regenerate with high fidelity in minimal time.Efficiency requires recovered content quality equal to or better than that obtainable by a classical receiver, without trading fidelity for minimalism.
From a bit-driven receiver to a knowledge-driven apprentice:: Reasoning-driven semantic networks emphasize knowledge and causality rather than only channels and communication resources, motivating a shift from data-driven to knowledge-driven design.The paper identifies causal and associational relationships as foundations for organizing information and building semantic languages.
From a bit-driven receiver to a knowledge-driven apprentice:: The paper proposes semantic metrics covering semantic impact, communication symmetry, and the reasoning capacity of a semantic communication link.These metrics are intended to characterize the semantic structure and reasoning capability of teacher–apprentice communication.
From a bit-driven receiver to a knowledge-driven apprentice:: Scaling semantic communication to cellular networks requires a reasoning plane between the control and user planes, using interventions and counterfactuals to support language acquisition.The proposed architecture also extends O-RAN toward real-time, near-real-time, and non-real-time intelligence.

D. Prior Works

Prior work has not yet provided a comprehensive foundation for building and scaling semantic communication systems. This tutorial addresses gaps in representation, semantic languages, causal reasoning, evaluation metrics, and large-scale deployment.

Existing surveys and tutorials cover communication modalities, multimodal transmission, and semantic signal processing, but leave important design fundamentals unresolved.The cited prior works do not collectively define the frameworks needed to build and deploy semantic communication systems.
Representation: Prior work does not specify preprocessing methods that transform entangled raw datastreams into learnable representations or enable apprentices to generate semantic content efficiently.The tutorial identifies representation as a missing layer between raw data and semantic-language learning.
Semantic Language: Existing studies acknowledge semantic languages but do not explicitly define their characteristics, efficiency benefits, or distinction from natural languages.The tutorial characterizes semantic languages by minimalism, generalizability, and efficiency and investigates how to build them gradually.
Reasoning and Causality: Prior art lacks concrete reasoning approaches and largely relies on statistical or associational relationships rather than causality.The tutorial uses interventions and counterfactuals to connect causal reasoning with recognizing root causes in datastreams.
Semantic KPIs: Classical KPIs such as rate, reliability, and latency do not capture radio-node reasoning or communication symmetry, motivating semantic evaluation metrics.The proposed reasoning capacity may exceed Shannon’s capacity because it relies on computing resources rather than only communication resources.
Scalable semantic communications: Prior studies mainly consider one source and destination, so the tutorial examines challenges and opportunities for scaling semantic communications across large-scale cellular networks.The paper organizes these topics into a tutorial framework covering the transition from classical systems, semantic representations, languages, and deployment.

A. What is NOT Semantic Communications?

Semantic communication differs from data compression and conventional AI-enabled wireless systems because it learns semantic structure, builds context-dependent knowledge, and supports reasoning. Its communication roles therefore shift from transmitting reconstructed bits toward teacher–apprentice knowledge construction.

Semantic communications is not data compression: Data compression minimizes a particular datastream by identifying and eliminating statistical redundancy, whereas semantic communication learns patterns representing structure and semantic content.Compression targets the current datastream and is reversible; semantic communication pursues representations that support broader tasks and reasoning.
Semantic communications is not data compression: Semantic communication achieves minimalism through semantic representations rather than merely reducing the number of transmitted bits.Organized knowledge enables radio nodes to draw more informed logical conclusions across the networking stack.
Semantic communications is not only an AI for wireless concept: Unlike classical AI-based transmitters that relay bits with added prediction, a semantic teacher attributes a semantic language to the raw bit pipeline.The semantic communication process changes how communication tasks are performed, including channel-related operations.
Semantic communications is not only an AI for wireless concept: Context consistency improves reasoning capability at the source and destination, whereas conventional data- or information-driven AI is not context-aware in this knowledge-based sense.The distinction concerns the logical theme surrounding learned data structures, not simply applying AI to an existing wireless task.
Semantic communications is not only an AI for wireless concept: Semantic communications gradually constructs a language between teacher and apprentice, allowing both to organize and build their knowledge bases.This differs from classical AI approaches trained beforehand on large datasets or through environmental trial and error.
Semantic communications is not only an AI for wireless concept: Reasoning-driven radio nodes can draw conclusions from knowledge bases and communicate their needs based on those conclusions.This capability distinguishes semantic communications from using an AI algorithm merely to optimize a wireless task.

4) Semantic communications is not only application-aware communications:

Semantic communications differs from application-aware communication by using low-level datastream semantics, causal and statistical relationships, and accumulated knowledge to support generalizable reasoning. It enables knowledge-driven radio nodes that can understand context, optimize across modalities, and operate with greater channel independence.

Application-aware communication targets use-case requirements, whereas semantic communication defines context from low-level datastream structure to support inter- and out-of-domain generalizability.Meaning learned for one service can improve end-to-end performance for another service.
Semantic communication combines causal and statistical relationships in data, unlike application-aware approaches that mainly rely on statistical learning.This supports challenges spanning multiple layers of the open systems interconnection model.
Metrics such as AoI and value-of-information remain use-case-specific and do not by themselves expose low-layer data structure or enable reasoning-driven knowledge bases.These significance measures remain dependent on communication resources and time-critical settings.
Semantic communication shifts AI-native wireless systems from data- or information-driven operation toward semantic languages, representations, and reasoning.The proposed direction replaces bit-pipeline communication with semantic content generation and representation-based reasoning.
Knowledge-driven radio nodes can use contextual information from communication, sensing, and tracking to optimize communication, control, and localization jointly.The accumulated knowledge base can also support logical decisions while offline.
Knowledge-based reasoning can reduce reliance on channel conditions by correcting inconsistent semantic representations rather than relying solely on error-correcting codes.Semantic languages also allow teachers to offload explanatory language to apprentices, reducing back-and-forth messaging.

4) Less Data, More Knowledge:

Semantic communication replaces bit-focused exchange with knowledge-driven interaction between a teacher and apprentice. Minimal semantic representations and reasoning can reduce redundant communication while improving the network’s ability to use context and accumulated knowledge.

Semantic representations use the fewest bits that still accurately describe a datastream’s semantic content.Minimalism also lets the apprentice infer conclusions from communicated context, reducing redundant back-and-forth messaging.
Semantic communication may reduce the need for additional spectrum and complex dynamic spectrum-sharing schemes as new technologies or use cases emerge.
As the semantic language matures, improved reasoning makes the communication link increasingly symmetric and less reliant on channel and communication resources.
A reasoning mechanism can turn communication from mere recovery into human-like exchange when messages contain exploitable patterns.
Unlike Shannon information, semantic information accounts for meaning, context, causal structure, and the complexity and maturity of the language shared by communicating agents.
Semantic information is defined by whether a datastream improves goal pursuit or expands the teacher-apprentice language through better reasoning.

2) From Entropy to Language Complexity:

Classical entropy measures uncertainty through binary-question complexity, but does not characterize the structure or meaning of a story or datastream. Semantic communication therefore replaces this focus with language complexity based on exchanging questions about content structure.

Classical entropy characterizes uncertainty by measuring the binary answers needed to determine a micro-state given a macro-state.
In the classical formulation, entropy depends on the transmitter’s probability choices rather than the message itself.
Human communication focuses on meaningful content and story structure rather than the number of yes/no questions required for exact reconstruction.
Semantic language complexity is proposed as the analogue of classical entropy, using exchanged questions to capture information structure and semantic content.

IV. BUILDING SEMANTIC COMMUNICATION SYSTEMS: REPRESENTATIONS AND LANGUAGES

Building a semantic communication system begins by separating learnable structure from purely random, memorizable detail. The learnable component follows a reasoning- and language-based path, while the memorizable component continues through classical communication processing.

Classical systems binary-encode, source-code, and channel-code raw data before reconstructing the transmitted message.
Semantic systems first scrutinize raw data and assign representations to its major structural parts instead of treating the entire datastream as a bulk.
The learnable component X_l contains semantic structure that can support reasoning and construction of a non-spurious semantic language.
The memorizable component X_m is governed by randomness, so learning it is complex and unproductive; it should use classical communication resources.
Learning all data points can overfit random details, producing spurious representations that fail to capture inherent structure or true semantic content.
Semantic languages and knowledge bases require organized knowledge, such as causal reasoning schemes and languages, rather than unstructured models and data.
After separation, X_m follows the classical path, whereas X_l undergoes reasoning to identify semantic content and representations before joint source-channel coding.

B. On the Structure and Complexity of a Semantic Language

A semantic language maps learnable data points to representations that enable the apprentice to generate conveyed content. Its complexity measures the tradeoff between modeling semantic structure and memorizing residual variability.

A semantic language L maps learnable data points X_l,i to semantic representations Z_i through identified semantic content elements Y_i.
Representations are designed to induce the apprentice to generate the original task or semantic content rather than merely reconstructing it.
The teacher identifies meanings and assigns or develops representations, while the apprentice uses computing and reasoning to generate content from them.
Language structure describes shared data characteristics, while variability describes how individual data points differ from that shared structure.
Language complexity Γ(L) captures the difficulty of identifying semantic content and learning representations for the learnable data.
The cross-entropy loss and Kolmogorov complexity jointly characterize the tradeoff between a language model’s predictive loss and complexity.
When random semantic-agnostic representations are learned, Γ(L) becomes very high because most of the data lacks structure.
The structure function becomes zero at sufficiently high complexity, after shared structure has been captured and remaining variability is memorized.

C. Why Semantic Languages? Why not Natural Languages?

A semantic language is designed around structured, efficient meaning rather than the formal syntax and context dependence of natural language. Its representations should be minimally sufficient, generalizable across domains and contexts, and more resource-efficient than classical communication.

Why semantic languages?: A semantic language need not be natural language because its purpose is to characterize causal and statistical datastream properties through representations.Natural-language syntax relies on deterministic grammatical rules, whereas semantic representations target datastream structure.
Semantics: Semantic languages reduce reliance on strict syntax and formalities to focus communication on meaning and minimize exchanged messages.The paper compares this with familiar vocabulary shared between people who can avoid formal language.
Pragmatics: Pragmatics are context-dependent and support dynamic reasoning, allowing reasoning learned in one scenario to generalize across settings when the language has matured.This capability requires minimized complexity while preserving structure and a known or stable communication context.
Key properties: Minimalism requires a representation to be minimally sufficient: oversimplification loses message structure, whereas overcomplexity captures unnecessary random information.The paper links excessive complexity to overfitting and low structure.
Key properties: Efficiency measures the time and communication resources gained relative to classical communication, using semantic impact as a proposed metric.Efficiency is treated as a required property of teacher-apprentice communication rather than a natural-language requirement.
Key properties: Generalizable representations are invariant to distribution, domain, and context, enabling application to unseen out-of-distribution data.The teacher must extract features whose structure remains invariant across contexts or domains.

D. On Existing Forms of Semantic Representation

Existing semantic-representation approaches offer useful capabilities but face limitations in expressivity, integration, scalability, or causal reasoning. The section motivates causal, knowledge-driven representations as a response to distribution shifts and weak generalization.

1) Natural language processing (NLP):: Natural-language processing is primarily suited to text, while describing holograms or high-precision manufacturing commands in natural language can require repetitive and redundant communication.The paper contrasts natural-language descriptions with coded mechanisms that can automate generation using computing resources.
2) On ANNs:: Artificial neural networks model statistical structure but cannot serve as the main semantic-representation block because they do not reason about causes, context, or effects.They may still model purely statistical processes such as channel and source-encoding variables.
3) Knowledge Graphs:: Knowledge graphs provide explainability and interpretability, but raw data is not naturally organized into separate categories and their limited expressivity constrains complex semantic messages.The proposed workflow requires disentangling raw data from memorizable information before causal discovery.
4) Topos:: Topos-based representations require major changes to existing machine-learning frameworks and may incur high computational complexity or intractability on raw data.The paper retains toposes as possible building blocks for settings requiring a many-world interpretation.
A. Motivation and Preliminaries: Existing generative methods mainly find associations in i.i.d. settings, although datastreams exchanged between wireless nodes are often not i.i.d.This mismatch is identified as a fundamental challenge for current approaches.
A. Motivation and Preliminaries: Surveyed frameworks show weak vertical and horizontal generalizability, cannot reliably draw logical conclusions, and struggle with novel domain and context settings.The stated limitations include distribution shifts, reasoning beyond generalization, and new representations with analogous prior patterns.
A. Motivation and Preliminaries: Causal inference distinguishes meaningful information from statistical accidents by examining the why and how of semantic content elements.The paper presents this shift as moving from information-driven to knowledge-driven or reasoning-driven systems.
4) Topos:: Causal inference must be adapted rather than applied plug-and-play to teacher and apprentice data, motivating scrutiny of causal representation-learning principles.The section then turns to reasoning through causality and semantic-language construction.

B. Fundamentals of Causal Reasoning

Causal reasoning is introduced by mapping a semantic language to a structural causal model, allowing teacher and apprentice to use associations, interventions, and counterfactuals. This supports interrogation, disentanglement, and construction of a shared knowledge base.

Causal language model: Mapping a semantic language to a structural causal model provides graphical models, structural equations, and causal mechanisms for reasoning over semantic representations.The paper presents SCMs as a way to formalize causality in the language-building and reasoning process.
Causal hierarchy: The causal hierarchy comprises associations, interventions, and counterfactuals, which are used to investigate correlation, causality, and stochastic changes.Interventions rank above associations in the causal hierarchy.
Interventions: An intervention is an apprentice question expressed with a do operator to characterize p(Z|do(X_l = x), A) for a representation model.The operator represents an action on X_l rather than conditioning on a subpopulation observed at X_l = x.
Queries in communication: Interventions and counterfactuals can replace some classical control and signaling datastreams and may support reverse mentorship through semantic transmission.The choice between classical and semantic transmission depends partly on the receiver’s reasoning foundations.
Counterfactuals: Counterfactuals occupy the highest causal-hierarchy level and let the apprentice ask why and what-if questions to build its knowledge base.Counterfactual language replaces the prior variability distribution p(ϵ) with the posterior p(ϵ|x).
Counterfactuals: An SCM-mapped language enables the apprentice to interrogate the teacher, with answers improving understanding and knowledge for logical reasoning.The paper identifies this interrogation capability as a benefit of constructing the language according to an SCM.
Disentanglement: Causal semantic representations disentangle each datastream and its representation, while interventions on one mechanism leave other mechanisms unchanged.This supports separating one learned task or content element from other transmitted information.
Disentanglement: Purely statistical models can entangle representations because a query on one representation may affect the others, unlike independent causal mechanisms.The causal formulation is associated with invariant, autonomous, and independent semantic representations.

C. Causality for Generalizable Representation Learning

Causal representation learning is presented as a route to semantic languages that remain stable across variability and unseen settings. The section emphasizes invariant structure, contrastive causal learning, and counterfactual invariance as mechanisms for generalization.

Generalizability: Causal representation learning provides tools for extracting minimal and efficient representations that generalize to unseen, out-of-domain, or distribution-shifted datastreams.The paper connects this generalizability to the universality of the semantic language.
Generalizability: A representation is generalizable when different causal queries produce the same representation for its corresponding content element.This criterion makes the apprentice’s semantic language consistent across queried situations.
Contrastive Causal Learning: Contrastive causal learning can identify invariant structure by teaching the apprentice to disentangle structure from variability before information exchange.The structure and variability are mapped to the content and style of an SCM.
Contrastive Causal Learning: A learned structure can generalize across variability such as dog breeds and locations while preserving the represented structure.The dog example is illustrative and is described as extensible to other data types.
Counterfactual Invariance: Counterfactual invariance constructs predictors invariant to particular perturbations in raw data and is stronger than invariance imposed through interventions.This stronger invariance is identified as important for semantic communications.

D. Challenges and Future Directions

The section identifies challenges in building reasoning-driven semantic communication systems, including causal-model construction, complexity, and query selection. It then introduces semantic communication metrics for evaluating teacher–apprentice symmetry and reasoning states.

Causal-model challenges: Causal models support semantic languages with intrinsic causality, but initializing them and expressing causal and statistical relationships remain difficult.Incorrect initialization can bias the semantic language, while statistical relationships can improve its generalizability.
Causal-model challenges: Highly complex structural causal models can make optimization difficult and jeopardize the explainability of the resulting knowledge base and semantic language.The reasoning node may struggle to balance representing data structure against model complexity.
Causal-model challenges: Apprentices must pose contextually appropriate interventions and counterfactuals, because irrelevant or biased questions can produce incorrect or selective semantic models.Providing context is suggested as a way to help apprentices formulate proper queries.
Semantic metrics: The proposed metrics evaluate teacher and apprentice reasoning capability, semantic reliance, and whether reverse mentorship has emerged.The framework is designed to assess equilibrium in which both sides can rely mainly on semantic-based transmissions.
Semantic metrics: Semantic impact measures the number of classical packets needed to regenerate a semantic content element from its representation.The communication symmetry index combines semantic impact with apprentice query packets and teacher raw-data packets.
Semantic metrics: The communication symmetry index distinguishes regimes ranging from little apprentice knowledge to semantic-dominant transmission and active apprentice intervention.When ηb,d,τ ≤1 and ιτ > 1, the system asymptotically resembles classical communication; other regimes describe increasing apprentice reasoning and semantic reliance.

B. From Information Capacity to Reasoning Capacity

The section argues that semantic networks require capacity measures beyond classical information capacity. It reframes information as semantic substance and transmission as an apprentice-side generative, reasoning-driven process, then defines reasoning capacity alongside classical capacity.

From information capacity: Classical information capacity measures the maximum achievable data rate under a specified bandwidth allocation, but semantic systems require a different capacity concept.The new metric must account for reasoning and semantic representations rather than only uncertainty-based transmission.
Semantic capacity foundations: Semantic information is defined as a semantic substance represented between teacher and apprentice, while transmission is treated as a reasoning-controlled generative process.The apprentice generates the conveyed meaning through manipulation rather than merely receiving propagated information.
Semantic capacity foundations: The proposed KPI measures reasoning through apprentice queries and efficiency through supplemented raw messages and semantic-representation impact.These dimensions characterize how much reasoning and raw-data support accompany semantic communication.
Reasoning capacity: Reasoning capacity is defined between a teacher and apprentice using the communication symmetry index and the server’s maximum computing capability.The index is computed per second and is independent of the semantic representation type.
Reasoning capacity: CT = CC + CR = W log2(1 + γ) + Ωlog2(1 + ηb,d) combines classical capacity with reasoning capacity.Classical capacity is limited by Shannon’s bound and bandwidth, whereas reasoning capacity is bounded by reasoning and computing resources.
Reasoning capacity: The section concludes that these metrics capture the convergence of communication and computing in semantic networks.The proposed framework extends end-to-end capacity accounting by including computation-enabled reasoning.

VII. SCALING SEMANTIC COMMUNICATIONS: FROM SEMANTIC LINKS TO SEMANTIC NETWORKS (6G AND BEYOND)

Scaling semantic communication from a single teacher–apprentice link to large networks requires mechanisms for uninformed teachers, heterogeneous computing, and suitable application contexts. The section identifies cooperative, reasoning-intensive systems as promising early settings while emphasizing unresolved AI and resource challenges.

Uninformed Teachers: Large-scale semantic networks must address cases where teachers lack the reasoning capability needed to develop and teach a semantic language.The teacher–apprentice symmetry assumption does not hold when radio nodes cannot independently construct the required language.
Uninformed Teachers: Reverse mentorship and cloud-based data showers are proposed ways to help uninformed teachers acquire representations and build a basic semantic language.An informed apprentice can teach specific representations through interventions and counterfactuals, while cloud services can provide complementary libraries.
Deployment Challenges: Semantic communication requires abundant end-device computing resources, but those resources remain heterogeneous and disparate across devices.Determining reasoning levels and performance gains in heterogeneous systems remains an open problem.
Early Use Cases: Applications requiring high service intelligence and autonomy may benefit more from semantic communication than applications focused only on operational intelligence.The section highlights complex or autonomous services as particularly suitable candidates.
Early Use Cases: Highly cooperative systems such as vehicle or robot swarms and digital twins can benefit from semantic languages for repeated control and synchronization.Semantic languages can reduce repetitive control messaging and support real-time physical–cyber replicas.
Early Use Cases: Early adoption is expected in goal-oriented services such as cyber-physical systems, digital twins, and connected robotics because they combine computing resources with strong cooperation.The required causal and associational AI frameworks remain in their infancy, making AI evolution an unresolved deployment requirement.

B. E2E Semantic Large-Scale Wireless Networks

The section sketches end-to-end semantic networks in which reasoning and computing become network-wide functions. It discusses semantic-based signaling, cooperative or competitive language formation, distributed computing, and an AI-native evolution of the physical layer and O-RAN.

Semantic-Based Networking: Semantic networking replaces classical acknowledgements and non-acknowledgements with apprentice queries such as interventions and counterfactuals.These queries are modeled as PDFs with do operators that help the apprentice learn the teacher’s structural causal model.
Physical Layer: In semantic networks, the physical layer serves as a language medium rather than merely mapping bits to symbols.Semantic communication re-engineers the physical layer instead of substituting for it.
Data-link and Networking Functions: Multiple access, multiplexing, resource allocation, and scheduling can retain classical structures while also being implemented through semantic queries or languages.Semantic implementations become more applicable as end nodes mature in their shared language.
Reasoning and Computing-based Optimization: Computing-resource scarcity may constrain semantic-network potential, making computing optimization, orchestration, and distribution central design problems.The section characterizes semantic-network evolution as a pursuit of computing resources analogous to earlier bandwidth expansion.
Ubiquitous Distributed Computing: Distributed computing can support heterogeneous devices by moving reasoning workloads toward local low-power nodes’ surrounding network resources.An IoT sensor may communicate classically while semantic communication operates from the edge to the digital twin, supporting fairness across heterogeneous UEs.
Competitive and Cooperative Languages: Scaling from cooperative teacher–apprentice pairs to multiple radio nodes introduces distinct goals and potentially noncooperative language formation.Understanding competitive and cooperative emergent languages is identified as important for large-scale scalability.

2) Semantic Communications in ORAN:

The tutorial connects semantic communications with O-RAN by introducing reasoning across multiple network timescales and outlining how semantic systems can reshape network operation. It also emphasizes coexistence with classical communication for unstructured information.

Semantic Communications in O-RAN:: O-RAN already places AI tasks at different execution speeds, with non-real-time RIC handling slower tasks and near-real-time RIC handling faster decisions.Examples include lifecycle management and orchestration for non-real-time operation, versus handover, QoS control, and load balancing for near-real-time operation.
Semantic Communications in O-RAN:: The proposed architecture highlights interactions among control, reasoning, and user planes within the central-unit layer.These interactions are presented as a key observation for 6G and beyond AI-native O-RAN.
Semantic Communications in O-RAN:: Semantic communications align O-RAN intelligence across real-time, near-real-time, and non-real-time network operation.The tutorial describes semantic communications as both a path toward AI-nativeness and a driving force for O-RAN.
Semantic Communications in O-RAN:: The reasoning plane makes O-RAN functions more intent-based by feeding back context and overall system goals.This feedback can inform xApps and rApps through identified semantic content elements.
Semantic Communications in O-RAN:: Manual operator requests can be automated and inferred from identified root causes of semantic content elements.The tutorial frames this as part of the techniques and facilitators needed to mature semantic systems.
Semantic Communications in O-RAN:: The tutorial identifies minimally sufficient representations, semantic language, causal reasoning, semantic-based KPIs, and judicious computing as five foundational tenets.These pillars are positioned within a roadmap for building next-generation semantic communication networks.
Semantic Communications in O-RAN:: Semantic networks and classical networks will need to coexist because raw datastreams contain random information that is better memorized than learned.The tutorial assigns such random information to classical communication channels while semantic structures handle semantic content elements.